Field of the Invention
The present invention relates to an embedded system for implementing a fast 3D camera system based on structured light, the fast 3D camera system based on structured light, and a method for acquiring 3D images using the system.
Description of the Related Art
Generally, technologies for reconstructing a 3D image of an object using a camera may be largely classified into active and passive techniques. As a typical example of active techniques, there are laser triangulation and a structured light-based method. As a typical example of passive techniques, there is stereo vision. Active techniques are mainly used for research or industrial purposes because they may achieve higher precision than passive techniques.
Meanwhile, a 3D camera using structured light is a modification of a stereo camera. Unlike a stereo camera, in which two identical cameras are used, a 3D camera based on structured light is configured such that one camera is replaced by a projection device such as a beam projector. Accordingly, in a 3D camera system based on structured light, a pattern is projected onto an object using a projection device, the object onto which the pattern is projected is captured using an image capturing device such as a camera, and 3D information is acquired by analyzing the captured image.
In other words, because a stereo camera system passively uses features extracted from an image but a structured light-based camera system actively projects a pattern onto an object using a projection device and uses the projected pattern to distinguish features, it may have a faster processing speed and higher spatial resolution. Due to these advantages, the structured light-based camera system is widely used in various fields such as object modeling and recognition, 3D measurement, industrial inspection, reverse engineering, and the like.
However, conventional 3D sensors based on structured light have the following problems.
First, if precise control on the nanosecond level is not achieved between a camera and a device for projecting patterns, a time delay may occur, but it is difficult to implement such nanosecond-level precise control. Also, the longer it takes to project a pattern and capture an image, the longer it takes to perform 3D sensing, making it difficult to acquire 3D data of a moving object. Furthermore, if no embedded system is provided, communication processing by the OS (Operating System) of a PC may incur an additional delay.
Alternatively, if an additional processor and an embedded device are used to control a camera and a projector, additional memory is required for pattern codecs for projection. This may increase the size of the processor and embedded device and incur additional costs.
Besides, because a 2D sensor, especially for an industrial robot, has an intelligent solution but has low positioning accuracy, a lot of calibration processes are required. These calibration processes are not suitable for modern automated systems, which rapidly change, and may pose an obstacle to the spread of industrial automation.
In other words, a 3D sensor used for an industrial robot must have a tolerance of error less than 1 mm and fast processing time, but such a sensor is not only very expensive but also large and heavy. For example, AICON's smartSCAN has high precision but the weight is 4 kg, the price is 100,000 to 150,000 dollars, and the measurement distance ranges from 6 to 26 cm. Due to the weight, it is difficult to mount on a hand of a robot unless the robot is an expensive robot with a high payload, and thus it is fixed to the surrounding environment. That is, because it remains in a stationary state, its usability suffers. As another example of a 3D sensor, there is Faro's Laser Scanner Focus3D, which has a tolerance of 2 mm and weighs 5 kg. This sensor has low precision, although it is a laser sensor, and has the same problem as the AICON's sensor due to the weight thereof. Meanwhile, Imetric's IScan M300 V70 is a lightweight mobile system that weighs 2.4 kg and has a measurement range from 23.5 to 75 cm. This system is promoted as a lightweight mobile system for solving the above problems, but its weight is similar to or greater than the payload of a low-cost industrial robot, making it difficult to mount on the hand of the robot.
In consideration of the above-mentioned problems, the implementation of a high-precision, high-accuracy, and lightweight 3D sensor is necessarily required in order to enable robots to replace humans in cellular manufacturing and visual inspection, which have depended on human labor. Also, as techniques for accurately recognizing objects using 3D images have come to be widely used, the implementation of such a 3D sensor is required.